However, an often more desirable approach to multispeaker TTS is to adapt a previously trained model to new speakers. One method named finetuning involves taking a pretrained TTS model, and then training it further on a target speaker, which is often done with less data and for fewer training iterations [1]}, [2]}, [3]}, [4]}. Although this saves the compute cost of training multiple target speaker specific models from scratch, one downside is that one finetuned model must be stored for each target speaker. This is often infeasible due to storage constraints and the difficulty of maintaining many models.
